Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "206" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 34 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 34 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460017 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.415841 | 5.843987 | -0.538781 | 2.607145 | 4.774853 | 2.286783 | 0.115389 | 0.439207 | 0.5294 | 0.4493 | 0.3448 | nan | nan |
| 2460016 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.054780 | 6.245144 | 0.117339 | 2.843455 | 1.493951 | 1.290207 | -0.365233 | 0.375905 | 0.5289 | 0.4617 | 0.3380 | nan | nan |
| 2460015 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.287720 | 6.342570 | 0.156169 | 3.046732 | 0.320084 | 1.332361 | -0.079708 | 0.366303 | 0.5400 | 0.4717 | 0.3381 | nan | nan |
| 2460014 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.514373 | 6.321173 | -0.775272 | 2.058908 | 8.935018 | 4.068979 | -0.315820 | 0.218039 | 0.5259 | 0.4298 | 0.3591 | nan | nan |
| 2460013 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.251758 | 6.428973 | 0.252730 | 2.970088 | 1.083660 | 1.562414 | 0.735853 | 0.826227 | 0.5312 | 0.4705 | 0.3412 | nan | nan |
| 2460012 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.298747 | 5.929675 | 0.185193 | 2.805215 | 1.103984 | 1.654234 | 0.875740 | 1.265368 | 0.5348 | 0.4770 | 0.3346 | nan | nan |
| 2460011 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.536022 | 6.205030 | -0.198219 | 3.615931 | 5.846941 | 4.562714 | 0.296871 | 0.761988 | 0.5559 | 0.4917 | 0.3411 | nan | nan |
| 2460010 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.762762 | 6.248900 | -0.156561 | 3.205538 | 0.920869 | 2.019638 | 0.009307 | 0.692012 | 0.5654 | 0.5093 | 0.3414 | nan | nan |
| 2460009 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.429510 | 5.178348 | 0.182243 | 3.670632 | 0.488510 | 2.049619 | -0.155012 | 1.054391 | 0.5689 | 0.5153 | 0.3434 | nan | nan |
| 2460008 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.342868 | 6.722762 | -0.437116 | 3.858035 | 1.805189 | 1.990328 | -0.568644 | 0.907227 | 0.6159 | 0.5682 | 0.3125 | nan | nan |
| 2460007 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.528354 | 6.078724 | -0.919299 | 3.109981 | 5.928701 | 1.506230 | -0.350540 | 0.427047 | 0.5904 | 0.5125 | 0.3490 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5942 | 0.5001 | 0.3208 | nan | nan |
| 2459998 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.190478 | 5.242656 | -0.436931 | 2.479416 | 6.379523 | 2.959871 | 0.074479 | 0.739796 | 0.5692 | 0.5011 | 0.3682 | nan | nan |
| 2459997 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.114785 | 5.646402 | -0.171266 | 2.747016 | 1.632697 | 2.494309 | 0.095738 | 0.784423 | 0.5728 | 0.5119 | 0.3679 | nan | nan |
| 2459996 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.344846 | 5.816574 | 0.316437 | 3.533676 | 0.385799 | 2.236927 | 1.047076 | 1.329350 | 0.5841 | 0.5236 | 0.3764 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.156812 | 5.993135 | -1.014019 | 2.955937 | 10.362091 | 2.676468 | 0.103536 | 0.446051 | 0.5899 | 0.5187 | 0.3745 | nan | nan |
| 2459994 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.682793 | 6.109840 | -1.263588 | 2.632031 | 8.687942 | 3.005220 | -0.339625 | 0.088087 | 0.5895 | 0.5103 | 0.3762 | nan | nan |
| 2459993 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.430880 | 6.234853 | -1.053324 | 2.214817 | 9.317541 | 3.834193 | -0.403065 | 0.479647 | 0.5816 | 0.4985 | 0.3960 | nan | nan |
| 2459991 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.972281 | 7.471100 | -0.767990 | 2.437314 | 3.656003 | 3.331147 | -0.463337 | 0.165994 | 0.5896 | 0.4893 | 0.4062 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.814111 | 6.428814 | -0.708886 | 2.288653 | 4.376362 | 3.936199 | -0.570762 | 0.002016 | 0.5919 | 0.4901 | 0.4006 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.029256 | 6.392809 | -0.578988 | 2.261048 | 7.478368 | 3.002510 | -0.096476 | 0.460755 | 0.5914 | 0.4964 | 0.3887 | nan | nan |
| 2459988 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.097876 | 7.362062 | -1.196354 | 2.208855 | 8.865361 | 5.004585 | -0.012065 | 0.850300 | 0.5915 | 0.5074 | 0.3751 | nan | nan |
| 2459987 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.525473 | 5.568688 | -0.788316 | 2.383871 | 2.552718 | 2.356491 | -0.730977 | 0.171758 | 0.5769 | 0.5053 | 0.3708 | nan | nan |
| 2459986 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.052134 | 6.864186 | -0.910431 | 2.483146 | 5.580969 | 4.164394 | -0.852241 | -0.223165 | 0.6118 | 0.5519 | 0.3251 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.065252 | 6.521904 | -0.529085 | 2.407300 | 1.592052 | 2.331001 | 0.013129 | 1.287680 | 0.5816 | 0.5125 | 0.3714 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.131554 | 6.010283 | -0.280913 | 2.524857 | 0.060005 | 3.210702 | -0.458938 | 0.042762 | 0.5975 | 0.5428 | 0.3444 | nan | nan |
| 2459983 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.771878 | 7.582569 | -1.429225 | 2.202667 | 10.069473 | 3.956650 | -0.088062 | 0.388942 | 0.6186 | 0.5569 | 0.3199 | nan | nan |
| 2459982 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.957998 | 6.020094 | -1.241351 | 2.144226 | 3.899779 | 1.625250 | -1.085499 | -0.428984 | 0.6893 | 0.6089 | 0.3015 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.139486 | 7.395568 | -0.609538 | 2.168912 | 3.901867 | 4.479489 | -0.527180 | 0.150645 | 0.6064 | 0.5043 | 0.3937 | nan | nan |
| 2459980 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.315757 | 6.915557 | -0.935542 | 2.035019 | 5.070659 | 3.661387 | -1.005245 | 0.334934 | 0.6526 | 0.5742 | 0.3177 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.192807 | 7.338532 | -0.516016 | 1.831706 | 0.314414 | 3.433725 | -0.375676 | 0.639598 | 0.6002 | 0.4971 | 0.3966 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.159870 | 7.544139 | -0.583275 | 1.946672 | 3.146538 | 4.254307 | -0.301860 | 0.707368 | 0.5999 | 0.4997 | 0.4007 | nan | nan |
| 2459977 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.037253 | 7.227418 | -0.950756 | 1.982874 | 10.849539 | 3.256194 | -0.127697 | 0.526477 | 0.5621 | 0.4753 | 0.3510 | nan | nan |
| 2459976 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.104555 | 7.326874 | -0.816127 | 1.937000 | 6.271808 | 3.228910 | -0.797776 | 0.031942 | 0.6084 | 0.5150 | 0.3856 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 5.843987 | 5.843987 | -0.415841 | 2.607145 | -0.538781 | 2.286783 | 4.774853 | 0.439207 | 0.115389 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.245144 | 6.245144 | 0.054780 | 2.843455 | 0.117339 | 1.290207 | 1.493951 | 0.375905 | -0.365233 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.342570 | 6.342570 | 0.287720 | 3.046732 | 0.156169 | 1.332361 | 0.320084 | 0.366303 | -0.079708 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 8.935018 | -0.514373 | 6.321173 | -0.775272 | 2.058908 | 8.935018 | 4.068979 | -0.315820 | 0.218039 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.428973 | 0.251758 | 6.428973 | 0.252730 | 2.970088 | 1.083660 | 1.562414 | 0.735853 | 0.826227 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 5.929675 | 0.298747 | 5.929675 | 0.185193 | 2.805215 | 1.103984 | 1.654234 | 0.875740 | 1.265368 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.205030 | 0.536022 | 6.205030 | -0.198219 | 3.615931 | 5.846941 | 4.562714 | 0.296871 | 0.761988 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.248900 | 0.762762 | 6.248900 | -0.156561 | 3.205538 | 0.920869 | 2.019638 | 0.009307 | 0.692012 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 5.178348 | 0.429510 | 5.178348 | 0.182243 | 3.670632 | 0.488510 | 2.049619 | -0.155012 | 1.054391 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.722762 | 6.722762 | 0.342868 | 3.858035 | -0.437116 | 1.990328 | 1.805189 | 0.907227 | -0.568644 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.078724 | -0.528354 | 6.078724 | -0.919299 | 3.109981 | 5.928701 | 1.506230 | -0.350540 | 0.427047 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 6.379523 | -0.190478 | 5.242656 | -0.436931 | 2.479416 | 6.379523 | 2.959871 | 0.074479 | 0.739796 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 5.646402 | 0.114785 | 5.646402 | -0.171266 | 2.747016 | 1.632697 | 2.494309 | 0.095738 | 0.784423 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 5.816574 | 0.344846 | 5.816574 | 0.316437 | 3.533676 | 0.385799 | 2.236927 | 1.047076 | 1.329350 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 10.362091 | -0.156812 | 5.993135 | -1.014019 | 2.955937 | 10.362091 | 2.676468 | 0.103536 | 0.446051 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 8.687942 | -0.682793 | 6.109840 | -1.263588 | 2.632031 | 8.687942 | 3.005220 | -0.339625 | 0.088087 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 9.317541 | -0.430880 | 6.234853 | -1.053324 | 2.214817 | 9.317541 | 3.834193 | -0.403065 | 0.479647 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 7.471100 | -0.972281 | 7.471100 | -0.767990 | 2.437314 | 3.656003 | 3.331147 | -0.463337 | 0.165994 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.428814 | 6.428814 | -0.814111 | 2.288653 | -0.708886 | 3.936199 | 4.376362 | 0.002016 | -0.570762 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 7.478368 | 6.392809 | -1.029256 | 2.261048 | -0.578988 | 3.002510 | 7.478368 | 0.460755 | -0.096476 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 8.865361 | 7.362062 | -1.097876 | 2.208855 | -1.196354 | 5.004585 | 8.865361 | 0.850300 | -0.012065 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 5.568688 | -0.525473 | 5.568688 | -0.788316 | 2.383871 | 2.552718 | 2.356491 | -0.730977 | 0.171758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.864186 | 6.864186 | -0.052134 | 2.483146 | -0.910431 | 4.164394 | 5.580969 | -0.223165 | -0.852241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.521904 | 6.521904 | 0.065252 | 2.407300 | -0.529085 | 2.331001 | 1.592052 | 1.287680 | 0.013129 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.010283 | 0.131554 | 6.010283 | -0.280913 | 2.524857 | 0.060005 | 3.210702 | -0.458938 | 0.042762 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 10.069473 | -0.771878 | 7.582569 | -1.429225 | 2.202667 | 10.069473 | 3.956650 | -0.088062 | 0.388942 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.020094 | -0.957998 | 6.020094 | -1.241351 | 2.144226 | 3.899779 | 1.625250 | -1.085499 | -0.428984 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 7.395568 | 7.395568 | -1.139486 | 2.168912 | -0.609538 | 4.479489 | 3.901867 | 0.150645 | -0.527180 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 6.915557 | 6.915557 | -1.315757 | 2.035019 | -0.935542 | 3.661387 | 5.070659 | 0.334934 | -1.005245 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 7.338532 | -1.192807 | 7.338532 | -0.516016 | 1.831706 | 0.314414 | 3.433725 | -0.375676 | 0.639598 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 7.544139 | 7.544139 | -1.159870 | 1.946672 | -0.583275 | 4.254307 | 3.146538 | 0.707368 | -0.301860 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | ee Temporal Variability | 10.849539 | -1.037253 | 7.227418 | -0.950756 | 1.982874 | 10.849539 | 3.256194 | -0.127697 | 0.526477 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | N19 | RF_ok | nn Shape | 7.326874 | 7.326874 | -1.104555 | 1.937000 | -0.816127 | 3.228910 | 6.271808 | 0.031942 | -0.797776 |